Ref. [1]} also describes a test performed using simulated binary black hole signals to validate the reliability of parameter estimates generated by PyCBC Inference with the emcee_pt sampler. The test is carried out by generating 100 realizations of stationary Gaussian noise colored by the power spectral densities of the Advanced LIGO detectors around the time of observation of GW150914 [2]}. A unique simulated binary black hole signal, whose parameters were sampled from the prior probability density function, is injected into each simulated noise realization. For the population of 100 simulated binary black hole signals, the network signal-to-noise ratios range from 5 to 160, and are predominantly spaced between 10 to 40. PyCBC Inference, using the emcee_pt sampler, was then run on each simulated binary black hole signal to produce samples of the posterior probability density function and compute credible intervals that estimate the modeled parameter values. For each parameter, we then calculate the percentage of the runs (\(x\%\) ) in which the true value of the parameter was recovered within a certain credible interval (\(y\%\) ). In the ideal case, there should be a 1-to-1 relation between these percentiles, ie. \(x\)  should equal \(y\)  for any value of the percentile \(y\) . The percentile-percentile curves obtained for each parameter in the test is plotted in Fig. 3 of Ref. [1]}. To evaluate the deviation between the percentile-percentile curve for each parameter from a 1-to-1 relation, a Kolmogorov-Smirnov (KS) test is performed. Using the set of p-values obtained for all the parameters, another KS test is performed expecting the p-values to adhere to a uniform distribution. The p-value obtained from this calculation is 0.7, which is sufficiently high to infer that PyCBC Inference, with it's implementation of the emcee_pt sampler, provides unbiased estimates of the binary black hole modeled parameters.
